d <- read.csv("../data/processed.csv")
d$LogRT <- log(d$RT)
names(d)
## [1] "X" "ID.true" "Word" "Label" "ConcValCombo"
## [6] "Task" "Response" "Accuracy" "EventTime" "Value"
## [11] "RT" "LogRT"
The goals of this analysis is to determine to what extent the differences we find hold within the sample of participants.
The piloting results tell us that there are indeed task differences for RT.
To that end we can ask:
What percentage of the participants show this effect?
What percentage of items showed this effect?
ggplot(d, aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
ggplot(d, aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4) +
facet_wrap(~ConcValCombo)
agr = d %>%
group_by(Task,ConcValCombo) %>%
summarize(MeanRT = mean(RT),
CILow = ci.low(RT),
CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow,
YMax = MeanRT + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanRT,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
agr = d %>%
group_by(Task,ConcValCombo) %>%
summarize(MeanAccuracy = mean(Accuracy),
CILow = ci.low(Accuracy),
CIHigh = ci.high(Accuracy)) %>%
mutate(YMin = MeanAccuracy - CILow,
YMax = MeanAccuracy + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanAccuracy,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
agr = d %>%
group_by(Task,ConcValCombo,Word) %>%
summarize(MeanAccuracy = mean(Accuracy))
## `summarise()` has grouped output by 'Task', 'ConcValCombo'. You can override
## using the `.groups` argument.
ggplot(agr, aes(x=MeanAccuracy, fill=ConcValCombo)) +
geom_density(alpha=.4) +
facet_wrap(~Task)
ggplot(agr, aes(x=MeanAccuracy, fill=Task)) +
geom_density(alpha=.4) +
facet_wrap(~ConcValCombo)
agr = d %>%
# filter(Task == "valence") %>%
group_by(Task,ConcValCombo,ID.true) %>%
summarize(MeanAccuracy = mean(Accuracy))
## `summarise()` has grouped output by 'Task', 'ConcValCombo'. You can override
## using the `.groups` argument.
ggplot(agr, aes(x=MeanAccuracy, fill=ConcValCombo)) +
geom_density(alpha=.4) +
facet_wrap(~Task)
ggplot(agr, aes(x=MeanAccuracy, fill=Task)) +
geom_density(alpha=.4) +
facet_wrap(~ConcValCombo)
Biggest differences for: disappoint, discourage, feeling, fogive, frown, laugh, sail, sing, smack, specialize, violate, wish
Least differences for: befall, behold, consider, crank, envy, hurl, impose, lick, meth, running, talk,
Some “surprising” differences - HURL: doesn’t seem to have a difference - VIOLATE: has a huge difference –> is the difference for VIOLATE a function of exteme emotive component as compared to HURL? Like, visceral
Generalization: In no case is the peak for Semantic Task === except for FEELING === higher than the peak for valence.
ggplot(d, aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4) +
facet_wrap(~Word,ncol=4)
agr = d %>%
group_by(Task,Word) %>%
summarize(MeanRT = mean(RT),
CILow = ci.low(RT),
CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow,
YMax = MeanRT + CIHigh)
## `summarise()` has grouped output by 'Task'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=Task,y=MeanRT,fill=Task)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=4) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
First, the clear cases where we see what we expected to find, then some other interesting cases
agr = d %>%
filter(Word == "violate") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.4 7.65 0.0791
## 2 valence 0.6 7.33 0.0176
ggplot(d[d$Word=="violate",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
agr = d %>%
filter(Word == "disappoint") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.35 7.54 0.0455
## 2 valence 0.45 7.31 0.00722
ggplot(d[d$Word=="disappoint",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
Should be clearly concrete, but accuracy at chance
agr = d %>%
filter(Word == "frown") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.45 7.54 0.0600
## 2 valence 0.45 7.34 0.0149
ggplot(d[d$Word=="frown",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
agr = d %>%
filter(Word == "laugh") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.6 7.45 0.0229
## 2 valence 0.45 7.31 0.0260
ggplot(d[d$Word=="laugh",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
agr = d %>%
filter(Word == "impose") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.5 7.54 0.0636
## 2 valence 0.45 7.51 0.0541
ggplot(d[d$Word=="impose",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
expected to be very concrete, and very negative - high variance
agr = d %>%
filter(Word == "hurl") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.4 7.58 0.0587
## 2 valence 0.4 7.53 0.0746
ggplot(d[d$Word=="hurl",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
agr = d %>%
filter(Word == "feeling") %>%
group_by(Task) %>%
summarize(MeanAccuracy = mean(Accuracy),
MeanLogRT = mean(LogRT),
Variance = var(LogRT))
print(agr)
## # A tibble: 2 × 4
## Task MeanAccuracy MeanLogRT Variance
## <chr> <dbl> <dbl> <dbl>
## 1 semantic 0.45 7.47 0.0583
## 2 valence 0.5 7.43 0.0256
ggplot(d[d$Word=="feeling",], aes(x=LogRT, fill=Task)) +
geom_density(alpha=.4)
agr = d %>%
filter(Task == "semantic") %>%
group_by(ConcValCombo,ID.true) %>%
summarize(MeanAccuracy = mean(Accuracy))
## `summarise()` has grouped output by 'ConcValCombo'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanAccuracy, fill=ConcValCombo)) +
geom_density(alpha=.4)
agr = d %>%
filter(Task == "semantic") %>%
group_by(ConcValCombo,ID.true) %>%
summarize(MeanLogRT = mean(LogRT),
MeanRT = mean(RT))
## `summarise()` has grouped output by 'ConcValCombo'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanLogRT, fill=ConcValCombo)) +
geom_density(alpha=.4)
ggplot(agr, aes(x=MeanRT, fill=ConcValCombo)) +
geom_density(alpha=.4)
Lots of differences, unlclear if anything is consistent
agr = d %>%
filter(Task == "semantic") %>%
group_by(ID.true,ConcValCombo) %>%
summarize(MeanRT = mean(RT),
CILow = ci.low(RT),
CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow,
YMax = MeanRT + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanRT,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ConcValCombo,y=MeanRT,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ID.true) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
LOTS of variance
agr = d %>%
filter(Task == "semantic") %>%
group_by(ID.true,ConcValCombo) %>%
summarize(MeanAccuracy = mean(Accuracy),
CILow = ci.low(Accuracy),
CIHigh = ci.high(Accuracy)) %>%
mutate(YMin = MeanAccuracy - CILow,
YMax = MeanAccuracy + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanAccuracy,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ConcValCombo,y=MeanAccuracy,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ID.true) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "semantic") %>%
group_by(ID.true,Word) %>%
summarize(MeanLogRT = mean(LogRT),
CILow = ci.low(LogRT),
CIHigh = ci.high(LogRT)) %>%
mutate(YMin = MeanLogRT - CILow,
YMax = MeanLogRT + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanLogRT,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=4) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "semantic") %>%
group_by(ID.true,Word) %>%
summarize(MeanAccuracy = mean(Accuracy),
CILow = ci.low(Accuracy),
CIHigh = ci.high(Accuracy)) %>%
mutate(YMin = MeanAccuracy - CILow,
YMax = MeanAccuracy + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanAccuracy,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=4) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "valence") %>%
group_by(ConcValCombo,ID.true) %>%
summarize(MeanAccuracy = mean(Accuracy))
## `summarise()` has grouped output by 'ConcValCombo'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanAccuracy, fill=ConcValCombo)) +
geom_density(alpha=.4)
agr = d %>%
filter(Task == "valence") %>%
group_by(ConcValCombo,ID.true) %>%
summarize(MeanLogRT = mean(LogRT),
MeanRT = mean(RT))
## `summarise()` has grouped output by 'ConcValCombo'. You can override using the
## `.groups` argument.
ggplot(agr, aes(x=MeanLogRT, fill=ConcValCombo)) +
geom_density(alpha=.4)
ggplot(agr, aes(x=MeanRT, fill=ConcValCombo)) +
geom_density(alpha=.4)
agr = d %>%
filter(Task == "valence") %>%
group_by(ID.true,ConcValCombo) %>%
summarize(MeanRT = mean(RT),
CILow = ci.low(RT),
CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow,
YMax = MeanRT + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanRT,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ConcValCombo,y=MeanRT,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ID.true) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "valence") %>%
group_by(ID.true,ConcValCombo) %>%
summarize(MeanAccuracy = mean(Accuracy),
CILow = ci.low(Accuracy),
CIHigh = ci.high(Accuracy)) %>%
mutate(YMin = MeanAccuracy - CILow,
YMax = MeanAccuracy + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanAccuracy,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ConcValCombo) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ConcValCombo,y=MeanAccuracy,fill=ConcValCombo)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~ID.true) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "valence") %>%
group_by(ID.true,Word) %>%
summarize(MeanRT = mean(RT), CILow = ci.low(RT), CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow, YMax = MeanRT + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanRT,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
agr = d %>%
filter(Task == "valence") %>%
group_by(ID.true,Word) %>%
summarize(MeanAccuracy = mean(Accuracy),
CILow = ci.low(Accuracy),
CIHigh = ci.high(Accuracy)) %>%
mutate(YMin = MeanAccuracy - CILow,
YMax = MeanAccuracy + CIHigh)
## `summarise()` has grouped output by 'ID.true'. You can override using the
## `.groups` argument.
dodge = position_dodge(.9)
ggplot(data=agr, aes(x=ID.true,y=MeanAccuracy,fill=ID.true)) +
geom_bar(position=dodge,stat="identity") +
facet_wrap(~Word,ncol=4) +
geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25,position=position_dodge(0.9))+
# ylim(5,8) +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 20, hjust = 1))
plot_by_word <- function(data) {
# Get the unique Words
unique_words <- unique(data$Word)
# Iterate over each Word and create a plot
for (word in unique_words) {
# Filter data for the current Word
data_filtered <- data %>%
filter(Word == word) %>%
group_by(Task, ID.true,Word) %>%
summarize(MeanRT = mean(RT),
CILow = ci.low(RT),
CIHigh = ci.high(RT)) %>%
mutate(YMin = MeanRT - CILow,
YMax = MeanRT + CIHigh)
# Generate the plot for the current Word
p <- ggplot(data_filtered, aes(x = ID.true, y = MeanRT, alpha = Task)) +
geom_bar(position=dodge,stat="identity") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = 0.25, position = position_dodge(0.9) ) + # Error bars
# facet_wrap(~Task) +
labs(title = paste("Mean RT for Word:", word),
x = NULL, # Remove x-axis label
y = "RT") + # Add y-axis label
guides(fill = "none")
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Tilt x-axis labels
# Print the plot
print(p)
}
}
# Usage Example (assuming `d` is your dataframe)
plot_by_word(d)
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.
## `summarise()` has grouped output by 'Task', 'ID.true'. You can override using
## the `.groups` argument.
## Warning: Using alpha for a discrete variable is not advised.